Optimal Drug Regimens for TB: An Integrated Computational/Experimental Approach

描述（由申请人提供）：该K25申请的候选人具有理论物理和计算机科学的背景。该候选人的长期职业目标是将生物科学领域的严格培训与他的定量和计算机技能相结合，以加速治疗结核病（TB）的后期药物开发过程。近期目标是开发改进的计算方法和工具，以确定临床前开发阶段的结核病新的最佳组合疗法。该职业发展奖被要求支持一项研究和培训计划，其中包括强化课程，辅导阅读，参加讲习班，会议和研讨会，以及一项研究计划，该计划提供了对结核病治疗的宿主-药物-病原体相互作用的充分理解。 候选人将得到科罗拉多州立大学（CSU）微生物学、免疫学和病理学系的支持。该提案的导师和合作者包括来自分枝杆菌研究实验室（MRL），环境与放射健康科学，化学与生物工程系以及兽医和生物医学学院临床科学系的教师。CSU是传染病研究的领导者，MRL拥有19名教师和100多名全职员工，是世界上最大的分支杆菌研究小组，为结核病研究领域提供了理想的环境，并为该项目的成功提供了条件。 为了为结核病新药方案的临床试验测试提供指导，显然需要一种有效的方法来确定临床前开发阶段的最佳组合方案。虽然传统的药代动力学/药效学（PK/PD）方法已被证明可用于确定最佳的单药抗微生物方案，但它们是数据密集型的，并且对于治疗TB所需的3-和4-药物方案的系统和全面评价的实用性有限。因此，本研究计划的目的和目标是开发和证明使用计算框架，提供最佳的组合药物剂量方案，用于治疗小鼠结核分枝杆菌感染。所提出的框架是一个新的整合生理学为基础的数学建模，贝叶斯推理，有针对性的实验研究，剂量优化和严格的方法。该提案的具体目标是：（1）开发小鼠TB感染模型中宿主-药物-病原体相互作用和药物剂量优化的计算框架，以及（2）证明使用计算框架确定小鼠TB感染模型中的最佳多药方案。虽然我们试图建立使用当前一线抗结核药物（异烟肼，利福平，吡嗪酰胺）的建议的可行性，该项目的最终目标是应用的方法，以较新的抗结核药物，以便在临床试验中进行测试的最佳方案的预测。

DESCRIPTION (provided by applicant): The candidate in this K25 application has a background in theoretical physics and computer science. The long term career goal of this candidate is to combine rigorous training in the field of bioscience with his quantitative and computer skills in order to accelerate the late-stage drug development process for the treatment of tuberculosis (TB). The immediate goal is to develop improved computational methods and tools to determine new optimal combination therapies for TB in the preclinical stage of development. This Career Development Award is requested to support a research and training program that includes intensive coursework, mentored readings, attendance at workshops, meetings and seminars, and a research plan that provides for a well-founded understanding of the host-drug-pathogen interactions for the treatment of TB. The candidate will be supported by the Department of Microbiology, Immunology and Pathology at Colorado State University (CSU). The mentors and collaborators for this proposal include faculty from the Mycobacteria Research Laboratories (MRL), the Department of Environmental & Radiological Health Sciences, Chemical & Biological Engineering, and the Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences. CSU is a leader in infectious disease research, and the MRL, with their 19 faculty and over 100 full-time staff, is the worlds largest group focused on mycobacterial research, providing an ideal environment to be introduced to the field of TB research, and for the success of this project. In order to provide guidance for clinical trial testing of new drug regimens for TB, there is a clear need for an efficient method to determine optimal combination regimens in the preclinical stage of development. While conventional pharmacokinetic/pharmacodynamic (PK/PD) methods have proven useful for the determination of optimal single drug antimicrobial regimens, they are data intensive and have limited utility for a systematic and thorough evaluation of the 3- and 4-drug regimens required to treat TB. The objective and goal of this research plan is therefore to develop and demonstrate the use of a computational framework which provides optimal combination drug dosage regimens for treatment of Mycobacterium tuberculosis infection in mice. The proposed framework is a novel integration of physiologically based mathematical modeling, Bayesian inference, targeted experimental studies, and a rigorous method for dose optimization. The specific aims of this proposal are to: (1) develop the computational framework for host-drug-pathogen interactions and drug dose optimization in mouse TB infection models, and (2) demonstrate the use of the computational framework for the determination of an optimal multidrug regimen in mouse TB infection models. While we seek to establish the feasibility of the proposal using current front-line anti-TB drugs (isoniazid, rifampin, pyrazinamide), the ultimate aim of this project is the application of the methodology to the newer anti-TB drugs in order to render predictions of optimal regimens for testing in clinical trials.